<p>Cell detection also creates some basic measurements of shape and staining intensity for each individual cell, 
which are useful for distinguishing between different populations of cells (here: classifying cells as tumor or non-tumor).</p>

<p>Additional features can be calculated and added to each cell to supplement this information
resulting in a more accurate classification.</p>


<details>
<summary>Screenshots</summary>

<div class="fig">
<img src="images/Measurement_map_orig.jpg" class="shadowed" />
<figcaption>
Original image containing tumor and non-tumor cells.
</figcaption>
</div>

<div class="fig">
<img src="images/Measurement_map_noisy.jpg" class="shadowed" />
<figcaption>
Visualization of 'nuclear-to-cell area ratio' measurements for individual cells.  Although this measurement is informative for distinguishing tumor from non-tumor cells, it is noisy.
</figcaption>
</div>

<div class="fig">
<img src="images/Measurement_map_smoothed.jpg" class="shadowed" />
<figcaption>
Visualization of 'nuclear-to-cell area ratio' measurements, smoothed across neighboring cells.  This reduces the noise of the raw measurement to make it more powreful in the correct identification of tumor and non-tumor regions.
</figcaption>
</div>

<div class="fig">
<img src="images/Measurement_map_Haralick.jpg" class="shadowed" />
<figcaption>
Visualization of Haralick texture features.
</figcaption>
</div>

<div class="fig">
<img src="images/Delaunay.jpg" class="shadowed" />
<figcaption>
Delaunay triangulation identifies neighboring cells, allowing the calculation of additional features based on cell clustering.
</figcaption>
</div>

</details>